scholarly journals P.072 The spectrotemporal characteristics of NMDA receptor encephalitis

2016 ◽  
Vol 43 (S2) ◽  
pp. S38-S38
Author(s):  
A Richard ◽  
T Zanos ◽  
F Dubeau ◽  
E de Villers-Sidani
Keyword(s):  
Nmda Receptor ◽  
Clinical Decision Making ◽  
Clinical Status ◽  
Power Spectra ◽  
Disease Status ◽  
Autoimmune Disorder ◽  
Clinical Decision ◽  
Disease Progress ◽  
Nmda Receptor Encephalitis ◽  
Before And After

Background: NMDA receptor encephalitis (NMDA-RE) is an autoimmune disorder caused by antibodies to the NR1-NR2B heterodimer of the NMDA receptor. Currently, disease status is tracked primarily by the presence of auto-antibodies in the cerebrospinal fluid (CSF) and serum. Using serological and CSF markers along with clinical parameters to track disease progress can be challenging since patient symptoms and disease progress can vary widely. Methods: EEGs were reviewed in a 31 year old male patient with proven NMDA-RE. EEG data were sampled from various times before and after diagnosis, as well as during various stages of treatment. All analyses were performed using Matlab (Mathworks). Results: We showed that using a simple 1/f model of spectral behaviour (Buzsaki and Draguhn, 2004), we could fit the power spectra of the raw data at various instances during routine EEGs. We have demonstrated that the values of specific fitting parameters vary in relationship to the patient’s clinical status across various stages of illness. Conclusions: The aim of this project was to explore the potential utility of EEG as a complement to the usual clinical metrics used in monitoring NMDA-RE. The analysis techniques presented here highlight the use of EEG as a practical, minimaly-invasive tool to monitor progress and potentially aid in clinical decision making.

108 Comparing autoimmune encephalitis variants in a pediatric cohort from Hamilton, Ontario

2021 ◽  
Vol 26 (Supplement_1) ◽  
pp. e76-e78
Author(s):  
Mark Grinberg ◽  
Michelle Schneeweiss ◽  
Christopher Povolo ◽  
Jeyanth Inkaran ◽  
Kevin Jones
Keyword(s):  
Nmda Receptor ◽  
Clinical Decision Making ◽  
Group Analysis ◽  
Autoimmune Encephalitis ◽  
Clinical Decision ◽  
Subject Area ◽  
Residual Symptoms ◽  
Receptor Antibody ◽  
Nmda Receptor Encephalitis ◽  
Csf Analysis

Abstract Primary Subject area Neurology Background Autoimmune Encephalitis (AE) is an emerging cause of epilepsy with numerous variants, including anti NMDA-receptor encephalitis, for which there is a detectable antibody. However, it is believed that there are many variants of AE for which an antibody has not yet been discovered. Objectives This study aimed to determine the differences in disease course of AE patients with and without detectable anti-NMDA receptor antibody. Design/Methods This retrospective analysis is part of a Canada-wide project aimed at evaluating the epidemiology and characteristics of AE. Cases with suspected AE were retrieved and screened by two independent reviewers against AE criteria. Those that met criteria were analyzed for trends and stratified into NMDA receptor antibody positive (NMDAr) and negative categories for inter-group analysis. Of 23 cases reviewed, 11 met criteria (aged 1-17 years, 27% males), of which 7 were NMDAr positive. Results The NMDAr subgroup was characterized by behavioural changes, focal seizures, and prodromal fever on presentation, whereas the receptor negative subset had a much higher variability of symptoms, without any distinctive patterns. On average, the NMDAr positive group showed an increase in white blood cell count on CSF analysis, and a slight increase in the proportion of patients presenting with supratentorial lesions on MRI. Both groups had abnormal findings on EEG. However, despite the lack of gross differences in findings, all of the NMDAr positive cases received IVIG (most with corticosteroids as well) while only 2 NMDAr negative patients received immunomodulatory therapy. At discharge 6/7 of the NMDAr patients had some form of residual movement disorder while the NMDAr negative group had more variable residual symptoms at discharge. Conclusion Our findings show that a high index of suspicion in the diagnosis of AE is required due to the indistinct distribution and variety in its presentation. Negative antibody findings should not rule out AE due to the possibility of unidentified antibodies. Future studies should explore why differences in treatment between the two groups exist, and if slight differences in presentation influence clinical decision-making.

scholarly journals Phylogenetic Analysis to Explore the Association Between Anti-NMDA Receptor Encephalitis and Tumors Based on microRNA Biomarkers

Biomolecules ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 572 ◽  
Author(s):  
Wang
Keyword(s):  
Phylogenetic Analysis ◽  
Nmda Receptor ◽  
Phylogenetic Trees ◽  
Tumor Resection ◽  
Autoimmune Disorder ◽  
Control Of Gene Expression ◽  
Nmda Receptor Encephalitis ◽  
Nmdar Encephalitis ◽  
Non Coding Rna ◽  
The Relationship

MicroRNA (miRNA) is a small non-coding RNA that functions in the epigenetics control of gene expression, which can be used as a useful biomarker for diseases. Anti-NMDA receptor (anti-NMDAR) encephalitis is an acute autoimmune disorder. Some patients have been found to have tumors, specifically teratomas. This disease occurs more often in females than in males. Most of them have a significant recovery after tumor resection, which shows that the tumor may induce anti-NMDAR encephalitis. In this study, I review microRNA (miRNA) biomarkers that are associated with anti-NMDAR encephalitis and related tumors, respectively. To the best of my knowledge, there has not been any research in the literature investigating the relationship between anti-NMDAR encephalitis and tumors through their miRNA biomarkers. I adopt a phylogenetic analysis to plot the phylogenetic trees of their miRNA biomarkers. From the analyzed results, it may be concluded that (i) there is a relationship between these tumors and anti-NMDAR encephalitis, and (ii) this disease occurs more often in females than in males. This sheds light on this issue through miRNA intervention.

Persistent seropositivity in oophorectomy-resistant anti-NMDA receptor encephalitis

2021 ◽  
Vol 14 (9) ◽  
pp. e241878
Author(s):  
Susmit Tripathi ◽  
Nara M Michaelson ◽  
Alan Segal
Keyword(s):  
Clinical Decision Making ◽  
Serum Antibody ◽  
Disease Patient ◽  
Clinical Decision ◽  
High Serum ◽  
Nmda Receptor Encephalitis ◽  
Resistant Disease ◽  
Nmdar Encephalitis ◽  
Antibody Titres ◽  
Case Basis

To discuss (1) the significance of seropositivity in anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis and (2) clinical decision making in oophorectomy resistant disease. Patient A (a 35-year-old woman) had high CSF and serum anti-NMDA antibody titres, a complicated hospital course, little improvement with first and second-line therapies, and remained with high CSF and serum antibody titres despite unilateral oophorectomy, requiring a nearly 13-month long hospitalisation. Conversely, patient B (a 29-year-old woman) had low CSF titres, seronegative disease and quickly recovered to her baseline with first line therapies and oophorectomy. Anti-NMDAR antibodies are themselves pathological, causing signalling dysfunction and internalisation of the NMDAR. Seropositivity with anti-NMDAR antibodies likely reflects leakage from the blood–brain barrier, with high serum titres being a downstream effect of high CSF titres. Empiric bilateral oophorectomies is controversial but appropriate on a case-by-case basis in extremely treatment-resistant NMDAR encephalitis given the possibility of antigenic microteratomas, which may not be detected on imaging or even bilateral ovarian biopsies.

scholarly journals SP2.2.9Using high fidelity simulation to improve hospital-based medical student education

2021 ◽  
Vol 108 (Supplement_7) ◽  
Author(s):  
Marc Huttman ◽  
Hui Fen Koo ◽  
Charlotte Boardman ◽  
Michael Saunders
Keyword(s):  
Medical Students ◽  
Clinical Decision Making ◽  
Medical Student Education ◽  
District General Hospital ◽  
Clinical Decision ◽  
Free Text ◽  
High Fidelity ◽  
High Fidelity Simulation ◽  
Qualitative Feedback ◽  
Before And After

Abstract Introduction The evidence shows that experiential learning has multiple benefits in preparing medical students for foundation training. An immersive ‘on call simulation’ session was designed for final-year medical students at a district general hospital. The aim of this project was to assess how beneficial the sessions were and how they can be improved. Methods Pairs of students received 12 bleeps over 2 hours directing them to wards where mock patient folders were placed. Students prioritised bleeps involving deteriorating patients, chasing results and dealing with nursing queries. Simulated senior input was available from the session facilitator. A structured debrief session allowed discussion of each case. Quantitative feedback was gathered using a sliding scale (measured in percentage) for confidence before and after the session. Qualitative feedback was gathered using a free-text box. Results Four sessions were held between October 2020 and January 2021 for a total of 28 students, of which 26 provided feedback. Average confidence increased from 38% to 66%. 96% of students were ‘extremely satisfied’ with the session. Feedback included: “Incredibly immersive and fun” and “I was made to think through my priorities and decisions”. Improvements could be made by using actors/mannequins to simulate unwell patients and by use of skills models. Conclusion High fidelity simulation training is valuable and should be considered a standard part of the student curriculum. It is particularly suited to final year students who have the required clinical knowledge for foundation training but are still developing confidence in clinical decision making and prioritisation.

Development and evaluation of simulation based neurosurgery curriculum. Pilot study at the Poznan University of Medical Sciences.

2018 ◽  
Vol 86 (4) ◽  
pp. 286
Author(s):  
Bartosz Sokół ◽  
Roman Jankowski ◽  
Barbara Więckowska ◽  
Łukasz Gąsiorowski ◽  
Michael Czekajlo
Keyword(s):  
Clinical Decision Making ◽  
Clinical Decision ◽  
Critically Ill Patient ◽  
Medical Sciences ◽  
Questionnaire Response ◽  
Human Patient ◽  
Patient Simulator ◽  
Response Data ◽  
Simulation Based ◽  
Before And After

Introduction. Neurosurgical emergencies are complex tasks. The current learning environment limits students’ ability to manage acute neurosurgical emergencies due to legal and safety concerns. Simulation provides an opportunity to participate in the care of neurosurgical emergencies and develop clinical decision making skills. Aim. We aim to determine whether neuroscience simulation curriculum improves student ability to: manage a critically ill patient, recognize neurosurgical emergencies, to assess how stress tolerance affects experience during simulations and effectiveness of students performance. The third objective is to develop a tool for student assessment.Material and Methods. The simulation was performed on SimMan 3G Human Patient Simulator (Laerdal Medical). Scenarios included common neurosurgical emergencies. Students were assessed before and after the course by completing a Likert type questionnaire. Response data was analysed using Cronbach’s reliability for Likert-type response data  and Spearman's monotonic correlation. Results. 60 students of fifth and sixth year of medical studies attended the course. 39 students of them replied to the questionnaire. The simulated clinical experience was positive and it improved their knowledge about neurosurgical emergencies. There was an improvement in their confidence. Improvement in individual and team performance was also observed.Conclusions. Neurosurgical simulations improve students` ability to recognize neurosurgical emergencies. The level of stress related to simulation is important factor of the education process and should be reduced to improve students’ development. Our questionnaire is an effective tool for assessment of students experience during clinical simulations.

scholarly journals Computerized nursing process in the Intensive Care Unit: ergonomics and usability

2016 ◽  
Vol 50 (6) ◽  
pp. 998-1004 ◽  
Author(s):  
Sônia Regina Wagner de Almeida ◽  
◽  
Grace Teresinha Marcon Dal Sasso ◽  
Daniela Couto Carvalho Barra ◽  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Nursing Practice ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Nursing Process ◽  
Evaluation Instrument ◽  
Before And After ◽  
Quasi Experimental ◽  
Usability Criteria

Abstract OBJECTIVE Analyzing the ergonomics and usability criteria of the Computerized Nursing Process based on the International Classification for Nursing Practice in the Intensive Care Unit according to International Organization for Standardization(ISO). METHOD A quantitative, quasi-experimental, before-and-after study with a sample of 16 participants performed in an Intensive Care Unit. Data collection was performed through the application of five simulated clinical cases and an evaluation instrument. Data analysis was performed by descriptive and inferential statistics. RESULTS The organization, content and technical criteria were considered "excellent", and the interface criteria were considered "very good", obtaining means of 4.54, 4.60, 4.64 and 4.39, respectively. The analyzed standards obtained means above 4.0, being considered "very good" by the participants. CONCLUSION The Computerized Nursing Processmet ergonomic and usability standards according to the standards set by ISO. This technology supports nurses' clinical decision-making by providing complete and up-to-date content for Nursing practice in the Intensive Care Unit.

scholarly journals Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via deep learning

2020 ◽  
Vol 4 (12) ◽  
pp. 1197-1207
Author(s):  
Wanshan Ning ◽  
Shijun Lei ◽  
Jingjing Yang ◽  
Yukun Cao ◽  
Peiran Jiang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Clinical Decision Making ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
Clinical Status ◽  
Clinical Decision ◽  
Viral Disease ◽  
Deep Learning Algorithm ◽  
Blood And Urine Samples ◽  
Ct Slices

AbstractData from patients with coronavirus disease 2019 (COVID-19) are essential for guiding clinical decision making, for furthering the understanding of this viral disease, and for diagnostic modelling. Here, we describe an open resource containing data from 1,521 patients with pneumonia (including COVID-19 pneumonia) consisting of chest computed tomography (CT) images, 130 clinical features (from a range of biochemical and cellular analyses of blood and urine samples) and laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) clinical status. We show the utility of the database for prediction of COVID-19 morbidity and mortality outcomes using a deep learning algorithm trained with data from 1,170 patients and 19,685 manually labelled CT slices. In an independent validation cohort of 351 patients, the algorithm discriminated between negative, mild and severe cases with areas under the receiver operating characteristic curve of 0.944, 0.860 and 0.884, respectively. The open database may have further uses in the diagnosis and management of patients with COVID-19.

scholarly journals Development, Implementation, and Evaluation of an In-Hospital Optimized Early Warning Score for Patient Deterioration

2020 ◽  
Vol 5 (1) ◽  
pp. 238146831989966 ◽  
Author(s):  
Cara O’Brien ◽  
Benjamin A. Goldstein ◽  
Yueqi Shen ◽  
Matthew Phelan ◽  
Curtis Lambert ◽  
...  
Keyword(s):  
Confidence Interval ◽  
Early Warning ◽  
Clinical Decision Making ◽  
Risk Model ◽  
Clinical Status ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Early Warning Score ◽  
Support Tool ◽  
National Early Warning Score

Background. Identification of patients at risk of deteriorating during their hospitalization is an important concern. However, many off-shelf scores have poor in-center performance. In this article, we report our experience developing, implementing, and evaluating an in-hospital score for deterioration. Methods. We abstracted 3 years of data (2014–2016) and identified patients on medical wards that died or were transferred to the intensive care unit. We developed a time-varying risk model and then implemented the model over a 10-week period to assess prospective predictive performance. We compared performance to our currently used tool, National Early Warning Score. In order to aid clinical decision making, we transformed the quantitative score into a three-level clinical decision support tool. Results. The developed risk score had an average area under the curve of 0.814 (95% confidence interval = 0.79–0.83) versus 0.740 (95% confidence interval = 0.72–0.76) for the National Early Warning Score. We found the proposed score was able to respond to acute clinical changes in patients’ clinical status. Upon implementing the score, we were able to achieve the desired positive predictive value but needed to retune the thresholds to get the desired sensitivity. Discussion. This work illustrates the potential for academic medical centers to build, refine, and implement risk models that are targeted to their patient population and work flow.

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